Direct Sampling
提出一种非MCMC的独立抽样方法，用于从后验分布中高效抽取样本，解决传统上依赖MCMC的抽样问题。
In recent years, Markov chain Monte Carlo (MCMC) methods have been used to provide a full Bayesian analysis both when the posterior distribution of interest is analytically intractable, and it is not known how to draw independent samples. In this article, a non-MCMC approach to sampling from posterior distributions is developed and illustrated. Some sampling problems, now thought to be best handled by MCMC methods alone, are tackled efficiently via independent samples. This article has supplementary material online.